AI Agent Systems: Architectures, Applications, and Evaluation
This survey is useful as scaffolding for the whole library. It organizes agent systems around model cores, memory, world models, planners, tool routers, critics, orchestration patterns, and deployment settings.
Why it matters
Agent evaluation is hard because long-horizon workflows are non-deterministic, tool-dependent, and sensitive to retry budgets, context growth, and environment variation. That is exactly the measurement problem ASI-oriented systems have to solve.
ASI relevance
As agents become the default interface for frontier models, architectural taxonomy becomes operational. It tells us what must improve: memory, planning, tool use, verification, guardrails, and reproducible evals under realistic workloads.